ArticlePLoS computational biology2022
Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Integrative functional optical imaging approaches: Optics, microfluidics, and machine learning for neuroscience in organoids and small-animal models.Biophysical journal · 2025Review
- Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans.Cell reports methods · 2025Article
- Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning inbioRxiv : the preprint server for biology · 2024Article
- Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.PLoS computational biology · 2024Article
- Unraveling the hierarchical structure of posture and muscle activity changes during mating ofPNAS nexus · 2024Article
- Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Correcting motion induced fluorescence artifacts in two-channel neural imaging.PLoS computational biology · 2022Article
- Imaging whole-brain activity to understand behavior.Nature reviews. Physics · 2022Article
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7 authors.
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Abstract
Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans. The constant motion and deformation of the nematode nervous system, however, impose a great challenge for consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving C. elegans. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2-dimensional neuronal regions are fused into 3-dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. With a small number of training samples, our bottom-up approach is able to process each volume-1024 × 1024 × 18 in voxels-in less than 1 second and achieves an accuracy of 91% in neuronal detection and above 80% in neuronal tracking over a long video recording. Our work represents a step towards rapid and fully automated algorithms for decoding whole brain activity underlying naturalistic behaviors.
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